import os
import cv2
import numpy as np
import base64
import json
from io import BytesIO
from PIL import Image
import tensorflow as tf
from tensorflow import keras
from fastapi import FastAPI, Request, Form, File, UploadFile
from fastapi.middleware.cors import CORSMiddleware
from fastapi.responses import JSONResponse
import uvicorn
import requests

emotion_model = None

app = FastAPI()

@app.on_event("startup")
def load_model():
    global emotion_model

    file_id = "1ist4U_0oCmHe7atGZvnxRCDMEGXCqqke"  # 🔁 Ganti dengan ID milikmu
    url = f"https://drive.google.com/uc?export=download&id={file_id}"
    output_path = "/tmp/moodDetection.keras"

    print("📥 Mengunduh model dari Google Drive...")
    response = requests.get(url)
    if response.status_code != 200:
        raise Exception("❌ Gagal mengunduh model dari Google Drive")

    with open(output_path, "wb") as f:
        f.write(response.content)

    print("✅ Model berhasil diunduh, memuat ke memory...")
    emotion_model = keras.models.load_model(output_path)
    print("✅ Model siap digunakan!")

emotion_labels = ['angry', 'disgust', 'fear', 'happy', 'neutral', 'sad', 'surprise']

face_cascade = cv2.CascadeClassifier(cv2.data.haarcascades + 'haarcascade_frontalface_default.xml')

app.add_middleware(
    CORSMiddleware,
    allow_origins=["*"],
    allow_credentials=True,
    allow_methods=["*"],
    allow_headers=["*"],
)

sessions = {}

@app.post("/api/deteksi-emosi")
async def detect_emotion(request: Request):
    try:
        form = await request.form()
        image_data = form.get("image")
        
        print(f"Received request with sessionId: {form.get('sessionId', 'not provided')}")
        print(f"Image data received: {bool(image_data)}")
        
        if image_data and "base64" in image_data:
            try:
                base64_data = image_data.split(',')[1]
                image_bytes = base64.b64decode(base64_data)
                
                print("Successfully decoded base64 data")
                
                img = Image.open(BytesIO(image_bytes))
                img_array = np.array(img)
                
                if len(img_array.shape) > 2 and img_array.shape[2] == 3:
                    gray = cv2.cvtColor(img_array, cv2.COLOR_RGB2GRAY)
                else:
                    gray = img_array
                
                print(f"Processed image shape: {gray.shape}")
                
                faces = face_cascade.detectMultiScale(gray, 1.3, 5)
                print(f"Detected {len(faces)} faces")
                
                if len(faces) > 0:
                    (x, y, w, h) = faces[0]
                    
                    face_roi = gray[y:y+h, x:x+w]
                    
                    resized_face = cv2.resize(face_roi, (48, 48))
                    normalized_face = resized_face / 255.0
                    reshaped_face = normalized_face.reshape(1, 48, 48, 1)
                    
                    prediction = emotion_model.predict(reshaped_face)
                    emotion_idx = np.argmax(prediction[0])
                    emotion = emotion_labels[emotion_idx]
                    confidence = float(prediction[0][emotion_idx])
                    
                    stress_mapping = {
                        'angry': 85, 'disgust': 65, 'fear': 70,
                        'sad': 75, 'surprise': 45, 'neutral': 30, 'happy': 15
                    }
                    
                    stress_level = stress_mapping.get(emotion, 50)
                    
                    session_id = form.get("sessionId", "default")
                    if session_id not in sessions:
                        sessions[session_id] = {
                            "emotions": [],
                            "stress_levels": []
                        }
                    
                    sessions[session_id]["emotions"].append(emotion)
                    sessions[session_id]["stress_levels"].append(stress_level)
                    
                    result = {
                        "emotion": emotion,
                        "confidence": confidence,
                        "stressLevel": stress_level,
                        "faceDetected": True,
                        "faceRegion": {"x": int(x), "y": int(y), "width": int(w), "height": int(h)}
                    }
                else:
                    result = {
                        "emotion": "unknown",
                        "confidence": 0,
                        "stressLevel": 0,
                        "faceDetected": False
                    }
            except Exception as e:
                print(f"Error processing image: {str(e)}")
                import traceback
                traceback.print_exc()
                result = {"error": f"Image processing error: {str(e)}"}
        else:
            result = {"error": "Invalid image data"}
    
    except Exception as e:
        print(f"Request handling error: {str(e)}")
        import traceback
        traceback.print_exc()
        result = {"error": f"Server error: {str(e)}"}
    
    response = JSONResponse(content=result)
    response.headers["Access-Control-Allow-Origin"] = "*"
    response.headers["Access-Control-Allow-Credentials"] = "true"
    return response

@app.get("/api/session-report/{session_id}")
async def session_report(session_id: str):
    if session_id not in sessions:
        return JSONResponse(content={"error": "Session not found"}, status_code=404)
    
    session_data = sessions[session_id]
    
    if session_data["emotions"]:
        emotion_counts = {}
        for emotion in session_data["emotions"]:
            if emotion in emotion_counts:
                emotion_counts[emotion] += 1
            else:
                emotion_counts[emotion] = 1
        
        dominant_emotion = max(emotion_counts, key=emotion_counts.get)
        
        avg_stress = sum(session_data["stress_levels"]) / len(session_data["stress_levels"])
        
        min_stress = min(session_data["stress_levels"])
        max_stress = max(session_data["stress_levels"])
        
        result = {
            "dominantEmotion": dominant_emotion,
            "emotionCounts": emotion_counts,
            "averageStressLevel": round(avg_stress, 2),
            "minStressLevel": min_stress,
            "maxStressLevel": max_stress,
            "totalFrames": len(session_data["emotions"])
        }
    else:
        result = {
            "error": "No data in session"
        }
    
    return JSONResponse(content=result)

if __name__ == "__main__":
    uvicorn.run("app:app", host="127.0.0.1", port=8080, reload=True)